A user identification method based on optical PUF authentication and related device

Abstract

The application discloses a user identification method based on optical PUF authentication and related devices, comprising: determining a first consistency parameter between a first response speckle and a first standard speckle corresponding to a first excitation code, to perform first user identification according to the first consistency parameter and a first identification threshold; after the first identification fails, performing second user identification according to a plurality of second consistency parameters and the first identification threshold; after the second identification fails, sequentially irradiating the PUF using a plurality of different second excitation codes to perform third user identification according to a plurality of third consistency parameters and the first identification threshold; after the identification still fails, configuring threshold adjustment, feature point comparison, biological identification and other means. The same and different excitation codes are used to irradiate and verify the user PUF in sequence, and a plurality of other means are configured to exclude system noise and human operation error interference from multiple angles, and improve the repeatability of user identification.

Description

Technical Field

[0001] This invention relates to the field of optical PUF authentication technology, and in particular to a user identification method based on optical PUF authentication, a user identification device based on optical PUF authentication, a user identification equipment based on optical PUF authentication, and a computer-readable storage medium. Background Technology

[0002] User identification based on Physical Unclonable Functions (PUFs) has received widespread attention in the field of hardware security. However, in practical applications, due to problems such as PUF positioning device malfunctions or user operational errors, legitimate users often face authentication failures. Therefore, to improve the repeatability of user identification and enhance system stability, it is not possible to rely solely on hardware positioning devices; instead, specific speckle recognition strategies are needed during user identification. However, traditional solutions typically rely on simple measurement methods to compare user speckles, which cannot meet the high repeatability requirements of practical applications. Therefore, how to provide a user identification method based on optical PUF authentication that can improve repeatability is an urgent problem that needs to be solved by those skilled in the art. Summary of the Invention

[0003] The purpose of this invention is to provide a user identification method based on optical PUF authentication with higher repeatability; another purpose of this invention is to provide a user identification device based on optical PUF authentication, a user identification equipment based on optical PUF authentication, and a computer-readable storage medium with higher repeatability.

[0004] To address the aforementioned technical problems, this invention provides a user identification method based on optical PUF authentication, comprising:

[0005] Illuminate the user's PUF with the first incentive code to obtain the first response speckle;

[0006] A first consistency parameter is determined between the first response speckle and the first standard speckle corresponding to the first excitation code, so as to perform the first user identification based on the first consistency parameter and the first identification threshold;

[0007] After the first user identification fails, the same first stimulus code is used to re-illuminate the user PUF, and multiple second response speckles are continuously acquired;

[0008] A second consistency parameter is determined between each of the second response speckles and the first standard speckle, so as to perform a second user identification based on the multiple second consistency parameters and the first identification threshold;

[0009] After the second user identification fails, multiple different second incentive codes are used to illuminate the user's PUF in sequence, and a third response speckle is obtained after each second incentive code is used to illuminate the user's PUF; each second incentive code corresponds to a second standard speckle.

[0010] A third consistency parameter is determined between each of the third response speckles and the second standard speckles corresponding to the second excitation code, so as to perform a third user identification based on the plurality of third consistency parameters and the first identification threshold;

[0011] After the third user identification fails, the first identification threshold is lowered to the second identification threshold;

[0012] The third consistency parameter is compared with the second identification threshold to perform a fourth user identification.

[0013] After the fourth user identification fails, the feature point set of any one of the first response speckle, the second response speckle, and the third response speckle, as well as the feature point set of the corresponding standard speckle, are determined based on the feature point matching algorithm, and the first similarity between the two sets of feature point sets is calculated.

[0014] The first similarity is compared with the third identification threshold to perform a fifth user identification.

[0015] After the fifth failed user identification attempt, biometric identification will be performed on the user.

[0016] Once biometric authentication is successful, activation information representing the user removing and reinserting the PUF is acquired, and the user's PUF is illuminated with a third excitation code based on the activation information to acquire a fourth response speckle pattern.

[0017] The feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third excitation code are determined based on the feature point matching algorithm, and the second similarity between the two feature point sets is calculated.

[0018] The second similarity is compared with the third identification threshold to perform a sixth user identification.

[0019] Optionally, after determining the feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, the method further includes:

[0020] Determine the feature point set that matches the feature point set of the fourth response speckle and the feature point set of the third standard speckle;

[0021] Obtain the first set of coordinate values ​​of the feature point set matched in the fourth response speckle, and the second set of coordinate values ​​of the feature point set matched in the third standard speckle;

[0022] The average displacement is determined based on the first set of coordinate values ​​and the second set of coordinate values.

[0023] Based on the displacement mean, all pixels in the fourth response speckle are translated to the target coordinate point to obtain the fifth response speckle; the fifth response speckle has an overlapping area with the fourth response speckle;

[0024] The authentication is performed based on the image of the fifth response speckle located in the overlapping region and the image of the third standard speckle located in the corresponding region, in order to perform the seventh user identification.

[0025] Optionally, calculating the first similarity between two sets of feature points includes:

[0026] Determine the number of feature points that match between the two sets of feature points;

[0027] The percentage of mutually matching feature points out of the total number of feature points is used as the first similarity.

[0028] And / or, calculating the second similarity between two sets of feature points includes:

[0029] Determine the number of feature points that match between the two sets of feature points;

[0030] The percentage of mutually matching feature points out of the total number of feature points is determined as the second similarity.

[0031] Optionally, performing a second user identification based on a plurality of second consistency parameters and the first identification threshold includes:

[0032] When any one of the second consistency parameters exceeds the first identification threshold, the second user identification is determined to be successful;

[0033] And / or, performing a third user identification based on a plurality of the third consistency parameters and the first identification threshold includes:

[0034] When any of the third consistency parameters exceeds the first identification threshold, the third user identification is determined to be successful;

[0035] And / or, performing a fourth user identification includes:

[0036] When any of the third consistency parameters exceeds the second identification threshold, the fourth user identification is determined to be successful.

[0037] Optionally, determining the first consistency parameter between the first response speckle and the first standard speckle corresponding to the first excitation code includes:

[0038] The Pearson correlation coefficient between the first response speckle and the first standard speckle corresponding to the first excitation code is determined as the first consistency parameter.

[0039] And / or, determining the second consistency parameter between each of the second response speckles and the first standard speckle includes:

[0040] The Pearson correlation coefficient between each of the second response speckles and the first standard speckle is determined as the second consistency parameter;

[0041] And / or, determining the third consistency parameter between each of the third response speckles and the second standard speckles based on the second excitation code includes:

[0042] The Pearson correlation coefficient between each of the third response speckles and the second standard speckles based on the second excitation code is determined as the third consistency parameter.

[0043] Optionally, determining the first consistency parameter between the first response speckle and the first standard speckle corresponding to the first excitation code includes:

[0044] Based on the image filtering algorithm, the first binarized texture feature image of the first response speckle and the second binarized texture feature image of the first standard speckle are determined;

[0045] Determine a first consistency parameter between the first binarized texture feature image and the second binarized texture feature image;

[0046] And / or, determining the second consistency parameter between each of the second response speckles and the first standard speckle includes:

[0047] Based on the image filtering algorithm, the third binarized texture feature image of each second response speckle is determined to be the same as the second binarized texture feature image of the first standard speckle.

[0048] Determine a second consistency parameter between the second binarized texture feature image and each of the third binarized texture feature images;

[0049] And / or, determining the third consistency parameter between each of the third response speckles and the second standard speckles based on the second excitation code includes:

[0050] Based on the image filtering algorithm, the fourth binarized texture feature image of each of the third response speckles and the fifth binarized texture feature image of each of the second standard speckles are determined.

[0051] A third consistency parameter is determined between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image.

[0052] Optionally, determining the first consistency parameter between the first binarized texture feature image and the second binarized texture feature image includes:

[0053] The Hamming distance between the first binarized texture feature image and the second binarized texture feature image is determined as the first consistency parameter;

[0054] Alternatively, the Pearson correlation coefficient between the first binarized texture feature image and the second binarized texture feature image can be determined as the first consistency parameter;

[0055] The second consistency parameter for determining the second binarized texture feature image and each of the third binarized texture feature images includes:

[0056] The Hamming distance between the second binarized texture feature image and each of the third binarized texture feature images is determined as the second consistency parameter;

[0057] Alternatively, the Pearson correlation coefficient between the second binarized texture feature image and each of the third binarized texture feature images can be determined as the second consistency parameter;

[0058] The third consistency parameter for determining each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image includes:

[0059] The Hamming distance between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image is determined as the third consistency parameter;

[0060] Alternatively, the Pearson correlation coefficient between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image is determined as the third consistency parameter. The present invention also provides a user identification device based on optical PUF authentication, comprising:

[0061] The first response speckle module is used to illuminate the user PUF with the first excitation code to obtain the first response speckle;

[0062] The first user identification module is used to determine the first correlation coefficient between the first response speckle and the first standard speckle corresponding to the first excitation code, so as to perform the first user identification based on the first correlation coefficient and the first identification threshold.

[0063] The second response speckle module is used to re-illuminate the user PUF using the same first excitation code after the first user identification fails, and to continuously acquire multiple second response speckles.

[0064] The second user identification module is used to determine the second correlation coefficient between each of the second response speckles and the first standard speckles, so as to perform a second user identification based on the multiple second correlation coefficients and the first identification threshold;

[0065] The third response speckle module is used to illuminate the user's PUF sequentially with multiple different second excitation codes after the second user identification fails, and to obtain the third response speckle after illuminating the user's PUF with each second excitation code; each second excitation code corresponds to a second standard speckle.

[0066] The third user identification module is used to determine the third correlation coefficient between each of the third response speckles and the second standard speckles corresponding to the second excitation code, so as to perform the third user identification based on the multiple third correlation coefficients and the first identification threshold.

[0067] The threshold reduction module is used to reduce the first recognition threshold to the second recognition threshold after the third user recognition fails.

[0068] The fourth user identification module is used to compare the third correlation coefficient with the second identification threshold to perform a fourth user identification.

[0069] The first similarity module is used to determine the feature points of any one of the first response speckle, the second response speckle, and the third response speckle, as well as the feature points of the corresponding standard speckle, based on the feature point matching algorithm after the fourth user identification fails, and to calculate the first similarity between the two sets of feature points.

[0070] The fifth user identification module is used to compare the first similarity with the third identification threshold to perform a fifth user identification.

[0071] The biometric module is used to perform biometric identification on the user after the fifth failed user identification attempt.

[0072] The fourth response speckle module is used to acquire activation information representing the user removing and reinserting the PUF after biometric recognition is successful, and to illuminate the user's PUF with a third excitation code according to the activation information to acquire the fourth response speckle.

[0073] The second similarity module is used to determine the feature points of the fourth response speckle and the feature points of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, and to calculate the second similarity between the two sets of feature points.

[0074] The sixth user identification module is used to compare the second similarity with the third identification threshold to perform a sixth user identification.

[0075] The present invention also provides a user identification device based on optical PUF authentication, the device comprising:

[0076] Memory: Used to store computer programs;

[0077] Processor: Used to implement the steps of the user identification method based on optical PUF authentication as described above when executing the computer program.

[0078] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the user identification method based on optical PUF authentication as described in any of the preceding claims.

[0079] The present invention provides a user identification method based on optical PUF authentication. After the user's PUF verification fails on the first attempt, the method will repeatedly verify the user's PUF by illuminating it with the same excitation code, and repeat the verification by changing the excitation code. If the verification fails again, the method will adjust the recognition threshold, extract speckle image feature points for comparison, and combine biometric identification to allow the user to re-insert and re-uninsert the PUF for feature point comparison. This method aims to eliminate interference from system noise and human operation errors from multiple perspectives and improve the repeatability of user identification.

[0080] The present invention also provides a user identification device based on optical PUF authentication, a user identification equipment based on optical PUF authentication, and a computer-readable storage medium, which also have the above-mentioned beneficial effects, and will not be described in detail here. Attached Figure Description

[0081] To more clearly illustrate the technical solutions of the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0082] Figure 1 A flowchart illustrating a user identification method based on optical PUF authentication provided in an embodiment of the present invention;

[0083] Figure 2 A flowchart illustrating a specific user identification method based on optical PUF authentication provided in this embodiment of the invention;

[0084] Figure 3 A flowchart illustrating another specific user identification method based on optical PUF authentication provided in this embodiment of the invention;

[0085] Figure 4 A flowchart illustrating yet another specific user identification method based on optical PUF authentication provided in this embodiment of the invention;

[0086] Figure 5 This is a schematic diagram illustrating the generation principle of the fifth response speckle pattern.

[0087] Figure 6 This is a structural block diagram of a user identification device based on optical PUF authentication provided in an embodiment of the present invention;

[0088] Figure 7 This is a structural block diagram of a user identification device based on optical PUF authentication provided in an embodiment of the present invention. Detailed Implementation

[0089] The core of this invention is to provide a user identification method based on optical PUF authentication. In the prior art, traditional solutions typically rely on simple measurement methods to compare user speckle patterns, which cannot meet the requirements for high robustness in practical applications.

[0090] The user identification method based on optical PUF authentication provided by this invention, after the user's PUF fails the first verification, will sequentially use the same excitation code to repeatedly illuminate the user's PUF for verification, change the excitation code to illuminate the user's PUF for verification and repeat the verification, and after all verifications fail, adjust the recognition threshold, extract speckle image feature points for comparison, and combine biometrics to ask the user to re-insert and uninsert the PUF and then compare the feature points again, so as to eliminate the interference of system noise and human operation errors from multiple angles and improve the repeatability of user identification.

[0091] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0092] Example 1

[0093] Please refer to Figure 1 , Figure 1 A flowchart of a user identification method based on optical PUF authentication provided in an embodiment of the present invention.

[0094] See Figure 1 In this embodiment of the invention, the user identification method based on optical PUF authentication includes:

[0095] S101: Illuminate the user PUF with the first excitation code to obtain the first response speckle.

[0096] The aforementioned user PUF is typically a device with a fixed physical form, and it can have a card-like structure or any other structure; no specific limitation is made here. The aforementioned excitation code (challenge) refers to a specific optical signal incident on the optical PUF. This optical signal is usually emitted by a light source and modulated or encoded. Illuminating the PUF with the encoded light will produce a speckle response; different excitation codes, i.e., different encoded optical signals, will produce different speckle responses when illuminating the PUF.

[0097] In this step, the user's PUF (Physically Unclonable Function) device is first illuminated with a pre-set first excitation code. This first excitation code can be a specific optical signal encoding, such as a laser signal with a certain wavelength, intensity, and modulation method. When this excitation code illuminates the user's PUF, a unique speckle image, i.e., the first response speckle, is generated due to the inhomogeneity of the PUF's internal microstructure. The process of acquiring this first response speckle in this step can be customized according to the actual situation; for example, it can be acquired using image acquisition devices such as cameras.

[0098] The first excitation code mentioned above can be an encoding of a specific optical signal randomly selected from the library. It can be generated by a laser modulator and has preset wavelength, intensity, and spatial mode. The random algorithm used in this random selection process can be determined according to the actual situation and is not specifically limited here.

[0099] The aforementioned first excitation code also corresponds to a first standard speckle pattern. This first standard speckle pattern is a speckle image obtained during system initialization, under ideal conditions or pre-set conditions, by illuminating the user's PUF with the first excitation code. It can serve as a reference benchmark in subsequent authentication processes. The aforementioned first response speckle pattern can be obtained by using a high-resolution industrial camera aimed at the PUF device. When the excitation code illuminates the device, the camera captures the speckle image and transmits it to the subsequent processing unit.

[0100] S102: Determine the first consistency parameter between the first response speckle and the first standard speckle corresponding to the first excitation code, so as to perform the first user identification based on the first consistency parameter and the first identification threshold.

[0101] After obtaining the first response speckle, this step requires comparing it with a pre-stored first standard speckle, which corresponds to the first excitation code. The aforementioned first consistency parameter typically characterizes the similarity between the first response speckle and the first standard speckle. For example, the Hamming distance or Pearson correlation coefficient between the first response speckle and the first standard speckle can be used as the first consistency parameter. The specific type of the first consistency parameter can be set according to the actual situation and is not specifically limited here. Specifically, this step may include: determining the Pearson correlation coefficient between the first response speckle and the first standard speckle corresponding to the first excitation code, as the first consistency parameter. That is, this step can directly calculate the Pearson correlation coefficient between the first response speckle and the first standard speckle to determine their similarity.

[0102] In this step, the first consistency parameter can be compared with the first identification threshold, or user identification can be performed through other methods, i.e., the first user identification. The specific content of the first identification threshold can be set according to the actual situation and is not specifically limited here. It is mainly used to determine whether the first consistency parameter meets the requirements and whether the current user PUF is the target user. The specific process of the first user identification includes: when the first consistency parameter exceeds the first identification threshold, the first user identification is determined to be successful.

[0103] It should be noted that the specific value of the identification threshold in this embodiment needs to be determined according to the type of consistency parameter. For example, the target threshold for Hamming distance can be 0.3; for Pearson correlation coefficient, the target threshold can be 0.7. Furthermore, it should be stated that when comparing the consistency parameter with the identification threshold in this application, the comparison standard is not simply based on the numerical value. For example, for the Pearson correlation coefficient, the closer the value is to 1, the closer the two speckles are. Therefore, it is necessary to determine that the user identification is successful only when the Pearson correlation coefficient is greater than the target threshold, and conversely, it is necessary to determine that the user identification is unsuccessful only when the Pearson correlation coefficient is less than or equal to the target threshold. Similarly, for Hamming distance, the closer the value is to 0, the closer the two speckles are. Therefore, it is necessary to determine that the user identification is successful only when the Hamming distance is less than the target threshold, and conversely, it is necessary to determine that the user identification is unsuccessful only when the Hamming distance is greater than or equal to the target threshold.

[0104] S103: After the first user identification fails, the user PUF is re-illuminated using the same first stimulus code, and multiple second response speckles are continuously acquired.

[0105] If the first user identification fails, it indicates that the similarity between the currently acquired first response speckle and the first standard speckle is not high enough. This insufficient similarity may be due to the influence of occasional system noise on speckle identification. Therefore, in this step, the same excitation code will be used, i.e., the first excitation code will be repeatedly used to illuminate the user PUF, and multiple response speckles will be acquired continuously. The response speckles acquired at this time are the second response speckles. The purpose of this is to reduce the influence of random factors on the identification results by acquiring the second response speckles multiple times, eliminate the influence of occasional system noise on speckle identification, and improve the accuracy of identification.

[0106] S104: Determine a second consistency parameter between each second response speckle and the first standard speckle, so as to perform a second user identification based on multiple second consistency parameters and a first identification threshold.

[0107] This step is basically similar to S102 above. In this step, it is necessary to determine the consistency parameter between each acquired second response speckle and the first standard speckle, i.e., the second consistency parameter. The specific data type of the second consistency parameter can be the same as or different from the first consistency parameter. That is, the second consistency parameter can be the Hamming distance or the Pearson correlation coefficient between each second response speckle and the first standard speckle, or it can be other parameters, which are not specifically limited here. Specifically, this step may include: determining the Pearson correlation coefficient between each second response speckle and the first standard speckle as the second consistency parameter. That is, this step can directly calculate the Pearson correlation coefficient between each second response speckle and the first standard speckle to determine the similarity between them.

[0108] In this step, the second consistency parameter corresponding to each second response speckle can be compared with the first identification threshold, or user identification can be performed through other methods, i.e., the second user identification. The specific content of the first identification threshold can be set according to the actual situation and is not specifically limited here. This second user identification process is mainly used to eliminate the influence of occasional system noise on speckle identification. The number of times the second response speckles are continuously acquired in this step can be three or more, and is not specifically limited here.

[0109] The specific process of the second user identification includes: when any one of the second consistency parameters exceeds the first identification threshold, the second user identification is determined to be successful. When any second consistency parameter exceeds the first identification threshold, it means that the user's PUF can be authenticated. Therefore, in this embodiment, as long as any second consistency parameter exceeds the first identification threshold, the second user identification can be determined to be successful. Conversely, when all second consistency parameters do not exceed the first identification threshold, the second user identification is determined to be unsuccessful.

[0110] S105: After the second user identification fails, the user PUF is illuminated sequentially using multiple different second stimulus codes, and a third response speckle is obtained after each second stimulus code is used to illuminate the user PUF.

[0111] In this embodiment of the invention, each of the second excitation codes corresponds to a second standard speckle pattern. If the second user identification fails, it means that after excluding occasional system noise, the similarity between the response speckles captured based on the first excitation code and the first standard speckle pattern is not high enough. The reason for this insufficient similarity may be due to human error or system noise affecting the standard speckle pattern during the registration stage. To eliminate the influence during the registration stage, multiple different excitation codes are used to sequentially illuminate the user's PUF in this step. The excitation code used in this step is the second excitation code. The number of excitation codes used in this step can be three or more, which is not specifically limited here. Correspondingly, three or more third response speckles can be obtained in this step.

[0112] In this step, multiple different excitation codes are used to sequentially illuminate the PUF, and the corresponding response speckle is acquired after each illumination, i.e., the third single-response speckle. In this embodiment, each different second excitation code corresponds to a pre-stored standard speckle, i.e., the second standard speckle. These standard speckles are typically obtained during the system initialization phase by illuminating the user's PUF with the corresponding excitation code. By using multiple different second excitation codes to acquire multiple third response speckles, the feature information of the user's PUF can be obtained from different angles and methods, eliminating the influence of human error or system noise during the registration phase on the standard speckles, and further improving the accuracy and reliability of recognition. For example, three different excitation codes can be used, with one third response speckle acquired after each excitation code, for a total of three third response speckles.

[0113] S106: Determine the third consistency parameter between each third response speckle and the second standard speckle corresponding to the second excitation code, so as to perform a third user identification based on multiple third consistency parameters and a first identification threshold.

[0114] In this embodiment, each third response speckle corresponds one-to-one with a second excitation code, and each second excitation code corresponds one-to-one with a second standard speckle. Therefore, in this embodiment, each third response speckle corresponds one-to-one with a second standard speckle. In this step, it is necessary to determine the consistency parameter between each third response speckle and its corresponding second standard speckle, i.e., the third consistency parameter. The specific data type of this third consistency parameter can be the same as or different from the first consistency parameter mentioned above. That is, the third consistency parameter can be the Hamming distance or the Pearson correlation coefficient between each third response speckle and the second standard speckle, or it can be other parameters, which are not specifically limited here. Specifically, this step may include: determining the Pearson correlation coefficient between each of the third response specks and the second standard speckle corresponding to the second excitation code, as the third consistency parameter. That is, this step can directly calculate the Pearson correlation coefficient between each third response speckle and its corresponding second standard speckle to determine the similarity between them.

[0115] In this step, the third consistency parameter corresponding to each third response speckle can be compared with the first identification threshold, or user identification can be performed through other methods, i.e., the third user identification. The specific content of the first identification threshold can be set according to the actual situation and is not specifically limited here. This third user identification process is mainly used to eliminate the influence of human operation errors or system noise during the registration stage on the standard speckle.

[0116] The specific process of the aforementioned third user identification includes: when any of the third consistency parameters exceeds the first identification threshold, the third user identification is determined to be successful. When any third consistency parameter exceeds the first identification threshold, it means that the user's PUF can be authenticated. Therefore, in this embodiment, as long as any third consistency parameter exceeds the first identification threshold, the third user identification can be determined to be successful. Conversely, when all third consistency parameters do not exceed the first identification threshold, the third user identification is determined to be unsuccessful.

[0117] S107: After the third user identification fails, the first identification threshold is lowered to the second identification threshold.

[0118] A third failed user identification means that, after ruling out occasional system noise and the impact of human error or system noise during the registration phase on the standard speckle pattern, the similarity between the corresponding speckle and the standard speckle is still insufficient. This insufficient similarity may be due to an excessively high identification threshold. To prevent legitimate users from being rejected by the system due to an excessively high threshold, this step requires lowering the first identification threshold to a second identification threshold within a reasonable range. The specific value of this reasonable range can be set according to the actual situation and is not specifically limited here.

[0119] It should be noted that the process of lowering the recognition threshold needs to be determined based on the specific type of the recognition threshold and the specific categories of each consistency parameter. For example, if the recognition threshold corresponds to the Pearson correlation coefficient, the lowering process will decrease the value of the first recognition threshold, for example, from 0.7 to 0.6; while if the recognition threshold corresponds to the Hamming distance, the lowering process will increase the value of the first recognition threshold, for example, from 0.3 to 0.4, in order to reduce the recognition standard.

[0120] S108: Compare the third consistency parameter with the second identification threshold to perform the fourth user identification.

[0121] Specifically, this step involves comparing the third consistency parameter with the lowered second identification threshold. The fourth user identification process includes: when any of the third consistency parameters is less than the second identification threshold, the fourth user identification is deemed successful. When any third consistency parameter exceeds the second identification threshold, it indicates that the user's PUF can be authenticated. Therefore, in this embodiment, as long as any third consistency parameter exceeds the second identification threshold, the fourth user identification is deemed successful; conversely, when none of the third consistency parameters exceed the second identification threshold, the fourth user identification is deemed unsuccessful.

[0122] S109: After the fourth user identification fails, the feature point set of any one of the first response speckle, the second response speckle, and the third response speckle, as well as the feature point set of the corresponding standard speckle, are determined based on the feature point matching algorithm, and the first similarity between the two sets of feature point sets is calculated.

[0123] The failure of the fourth user identification means that the PUF authentication based on the consistency parameter has failed the user identification. Therefore, this step requires calculating the feature points that match the response speckle and the standard speckle based on the feature point matching algorithm to obtain the similarity based on the feature points, i.e. the first similarity.

[0124] Feature point matching algorithms typically consist of at least three steps: the first step is to detect feature points in the image; the second step is to extract descriptors for each feature point; and the third step is to match two sets of feature points based on the descriptors. When performing feature point detection, algorithms are usually used to detect feature points in both the first response speckle and standard speckle patterns. The detected feature points are typically described as feature vectors or descriptors. Feature points represent key information in an image and are usually invariant to rotation, scale, and illumination changes, allowing them to be accurately detected at different positions and angles within the image. Common feature point detection algorithms include the Harris corner detection algorithm and the FAST (Features from Accelerated Segment Test) algorithm. The appropriate algorithm should be selected based on the specific application requirements and the characteristics of the speckle image (such as noise type and viewpoint variations).

[0125] After feature point detection and descriptor extraction, we typically obtain the feature point set of the selected response speckle and the corresponding feature point set of the standard speckle. During feature point matching, we calculate the similarity between feature point descriptors to find similar feature point pairs in the two images. That is, we find similar feature points from the two feature point sets to form feature point pairs. The two feature points constituting a feature point pair are the matching feature points in the selected response speckle and the corresponding standard speckle.

[0126] In this step, the first similarity can be determined based on the number of mutually matching feature points in the two sets of feature points. Specifically, this step can include: determining the number of mutually matching feature points between the two sets of feature points; and determining the percentage of mutually matching feature points out of the total number of feature points, as the first similarity. In other words, this step can use the ratio of mutually matching feature points out of the total number of feature points as the percentage. This can be achieved by calculating the percentage of feature points corresponding to the standard speckle in the response speckle out of the total number of feature points in the response speckle, or the percentage of feature points corresponding to the response speckle in the standard speckle out of the total number of feature points in the standard speckle, or the percentage of all mutually matching feature points in both images out of the total number of feature points in the foreground of both images. When the number of feature points selected for the response speckle is equal to the number of feature points in the corresponding standard speckle, the above three percentage values ​​are equal.

[0127] S110: Compare the first similarity with the third identification threshold to perform the fifth user identification.

[0128] This step specifically compares the first similarity score with the third identification threshold. This third identification threshold can be the same as or different from the second identification threshold, depending on the specific circumstances. The fifth user identification process includes: if the first similarity score is less than the second identification threshold, the fourth user identification is considered successful. If the first similarity score is less than the third identification threshold, the user's PUF (User Identity Function) can be authenticated; conversely, if the first similarity score is greater than or equal to the third identification threshold, the fifth user identification is considered unsuccessful.

[0129] S111: Perform biometric identification on the user after the fifth failed user identification attempt.

[0130] The fifth failed user identification attempt means that other methods of user identification are needed, not just relying on the user's PUF (User Identity Frame). This step specifically involves biometric identification, verifying whether the user's biometric features match pre-stored biometric data. These biometric features can be fingerprints, facial recognition, iris scans, etc., and are not specifically limited here. This biometric identification step attempts to confirm the user's identity.

[0131] If biometric authentication fails, it means that neither the user nor their PUF (Personalized Activated Function) has been pre-registered, and access will be denied. If biometric authentication succeeds, it confirms that the user has pre-registered, but the PUF they used failed the initial verification process, and subsequent steps will be executed.

[0132] S112: After biometric identification is successful, obtain activation information indicating that the user has removed and reinserted the PUF, and use the third excitation code to illuminate the user's PUF according to the activation information to obtain the fourth response speckle.

[0133] In this step, once the biometric authentication is successful, it means that the user has registered in advance. In this embodiment, the user can remove the PUF and reinsert it into the system. Therefore, in this step, the activation information of the user removing and reinserting the PUF can be obtained. The specific content and form of the activation information can be set according to the actual situation and are not specifically limited here.

[0134] In this step, the user's PUF is illuminated using a third stimulus code based on the activation information to obtain a fourth response speckle. This third stimulus code must be distinct from the stimulus code used to generate the response speckle selected in S109 above, and it typically needs to be distinct from the first and second stimulus codes mentioned above. The response speckle generated in this step based on the third stimulus code illuminating the user's PUF is the fourth response speckle, and the pre-stored standard speckle corresponding to the third stimulus code is the third standard speckle. This third standard speckle is typically the speckle image obtained by illuminating the user's PUF with the third stimulus code during the system initialization phase.

[0135] S113: Based on the feature point matching algorithm, determine the feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third excitation code, and calculate the second similarity between the two feature point sets.

[0136] This step is similar to S109 above. It determines the feature points of the fourth response speckle and the third standard speckle based on the feature point matching algorithm. For details on the feature matching algorithm, please refer to the previous content, which will not be repeated here. Correspondingly, the calculation of the second similarity is also the same as the calculation process of the first similarity. In this step, the number of feature points that match each other between the feature points of the fourth response speckle and the feature points of the third standard speckle will be determined, and then the percentage of the number of matching feature points to the total number of feature points will be determined as the second similarity.

[0137] S114: Compare the second similarity with the third identification threshold to perform the sixth user identification.

[0138] This step specifically compares the second similarity score with the third identification threshold. The sixth user identification process includes: if the second similarity score is less than the second identification threshold, the sixth user identification is considered successful. If the second similarity score is less than the third identification threshold, the user's PUF (User Identity Function) can be authenticated; conversely, if the second similarity score is greater than or equal to the third identification threshold, the sixth user identification is considered unsuccessful.

[0139] The user identification method based on optical PUF authentication provided in this embodiment of the invention, after the user's PUF fails the first verification, will sequentially use the same excitation code to repeatedly illuminate the user's PUF for verification, change the excitation code to illuminate the user's PUF for verification and repeat the verification, and after all verifications fail, adjust the recognition threshold, extract speckle image feature points for comparison, and combine biometrics to ask the user to re-insert and uninsert the PUF and then compare the feature points again, so as to eliminate the interference of system noise and human operation errors from multiple angles and improve the repeatability of user identification.

[0140] The specific details of the user identification method based on optical PUF authentication provided by this invention will be described in detail in the following embodiments, and will not be repeated here.

[0141] Example 2

[0142] Please refer to Figure 2 , Figure 2 The flowchart illustrates a specific user identification method based on optical PUF authentication provided in this embodiment of the invention.

[0143] See Figure 2 In this embodiment of the invention, the user identification method based on optical PUF authentication includes:

[0144] S201: Illuminate the user's PUF with the first excitation code to obtain the first response speckle.

[0145] This step is basically the same as S101 in the above embodiment. For details, please refer to the above embodiment. It will not be repeated here.

[0146] S202: Based on the image filtering algorithm, determine the first binarized texture feature image of the first response speckle and the second binarized texture feature image of the first standard speckle.

[0147] In this step, image filtering algorithms such as Gabor filtering can be used to process the two types of speckle images mentioned above. For details on image filtering algorithms such as Gabor filtering, please refer to existing technologies; they will not be elaborated upon here. Specifically, in this step, the first response speckle and the first standard speckle can be processed separately using the Gabor filtering algorithm to obtain a first binarized texture feature image corresponding to the first response speckle and a second binarized texture feature image corresponding to the first standard speckle.

[0148] S203: Determine a first consistency parameter between the first binarized texture feature image and the second binarized texture feature image, so as to perform the first user identification based on the first consistency parameter and the first recognition threshold.

[0149] A consistency parameter characterizes the degree of consistency or similarity between the two binarized texture feature images. This primary parameter can be either the Pearson correlation coefficient or the Hamming distance, etc. For the specific calculation process of Hamming distance or Pearson correlation coefficient, please refer to existing techniques, which will not be elaborated here. This consistency parameter characterizes the degree of difference between the first and second binarized texture feature images.

[0150] Taking Hamming distance as an example, the smaller the Hamming distance, the higher the similarity between the two images. In this step, when the Hamming distance is less than the first recognition threshold, it means that the first response speckle and the first standard speckle have a high degree of similarity, and it can be determined that the user authentication is successful. Conversely, when the Hamming distance is greater than or equal to the first recognition threshold, it means that the first response speckle and the first standard speckle have a large difference, and it can be determined that the user authentication is unsuccessful. This step can specifically include: determining the Hamming distance between the first binarized texture feature image and the second binarized texture feature image, as the first consistency parameter.

[0151] Taking the Pearson correlation coefficient as an example, a higher Pearson correlation coefficient indicates a higher similarity between two images. In this step, when the Pearson correlation coefficient is greater than the first recognition threshold, it means that the first response speckle and the first standard speckle have a high degree of similarity, and correspondingly, user authentication can be determined to be successful. Conversely, when the Pearson correlation coefficient is less than or equal to the first recognition threshold, it means that the first response speckle and the first standard speckle have a large difference, and correspondingly, user authentication can be determined to be unsuccessful. This step can specifically include: determining the Pearson correlation coefficient between the first binarized texture feature image and the second binarized texture feature image, as the first consistency parameter.

[0152] The aforementioned first identification threshold needs to correspond to the type of the first consistency parameter. For example, the first identification threshold can be 0.3 for Hamming distance and 0.7 for Pearson correlation coefficient. No specific limitation is made here.

[0153] S204: After the first user identification fails, the user PUF is re-illuminated using the same first stimulus code, and multiple second response speckles are continuously acquired.

[0154] This step is basically the same as S103 in the above embodiment. For details, please refer to the above embodiment. It will not be repeated here.

[0155] S205: Based on the image filtering algorithm, determine the third binarized texture feature image of each second response speckle and the second binarized texture feature image of the first standard speckle.

[0156] In this step, image filtering algorithms such as Gabor filtering can be used to process each second response speckle and the first standard speckle, thereby obtaining multiple third binarized texture feature images that correspond one-to-one with the second response speckle, and second binarized texture feature images that correspond to the first standard speckle.

[0157] S206: Determine a second consistency parameter between the second binarized texture feature image and each of the third binarized texture feature images, so as to perform a second user identification based on the multiple second consistency parameters and the first identification threshold.

[0158] In this step, a consistency parameter can be determined for each third binarized texture feature image and the second binarized texture feature image, denoted as the second consistency parameter. Multiple second consistency parameters can be obtained in this step. This second consistency parameter characterizes the degree of difference between the third binarized texture feature image and the second binarized texture feature image. In this step, the Hamming distance or Pearson correlation coefficient between the speckle patterns can be calculated as the second consistency parameter. Specifically, this step can include determining the Hamming distance between the second binarized texture feature image and each of the third binarized texture feature images as the second consistency parameter; or this step can include determining the Pearson correlation coefficient between the second binarized texture feature image and each of the third binarized texture feature images as the second consistency parameter.

[0159] In this step, when any of the second consistency parameters exceeds the first identification threshold, it means that there is a high degree of similarity between the second response speckle and the first standard speckle, and it can be determined that the user authentication is successful. Conversely, when none of the second consistency parameters exceed the first identification threshold, it means that there is a significant difference between each second response speckle and the first standard speckle, and it can be determined that the user authentication is unsuccessful.

[0160] S207: After the second user identification fails, the user PUF is illuminated sequentially using multiple different second stimulus codes, and a third response speckle is obtained after each second stimulus code is used to illuminate the user PUF.

[0161] This step is basically the same as S105 in the above embodiment. For details, please refer to the above embodiment. It will not be repeated here.

[0162] S208: Based on the image filtering algorithm, determine the fourth binarized texture feature image of each third response speckle and the fifth binarized texture feature image of each second standard speckle.

[0163] In this step, image filtering algorithms such as Gabor filtering can be used to process each third response speckle and each second standard speckle, thereby obtaining multiple fourth binarized texture feature images that correspond one-to-one with the third response speckle, and multiple fifth binarized texture feature images that correspond one-to-one with the second standard speckle. Since the third response speckle and the second standard speckle have a one-to-one correspondence, the aforementioned fourth and fifth binarized texture feature images also have a one-to-one correspondence.

[0164] S209: Determine the third consistency parameter between each fourth binarized texture feature image and the corresponding fifth binarized texture feature image, so as to perform a third user identification based on multiple third consistency parameters and a first identification threshold.

[0165] In this step, a third consistency parameter can be determined for each fourth binarized texture feature image and its corresponding fifth binarized texture feature image. Multiple third consistency parameters can be obtained in this step. This third consistency parameter characterizes the degree of difference between the fourth and fifth binarized texture feature images. In this step, the Hamming distance or Pearson correlation coefficient between the speckle patterns can be calculated as the third consistency parameter. Specifically, this step can include determining the Hamming distance between each of the fourth binarized texture feature images and its corresponding fifth binarized texture feature image as the third consistency parameter; or this step can include determining the Pearson correlation coefficient between each of the fourth binarized texture feature images and its corresponding fifth binarized texture feature image as the third consistency parameter.

[0166] In this step, when any third consistency parameter exceeds the first identification threshold, it means that there is a high degree of similarity between the third response speckle and the first standard speckle, and it can be determined that the user authentication is successful. Conversely, when none of the third consistency parameters exceed the first identification threshold, it means that there is a significant difference between each third response speckle and the first standard speckle, and it can be determined that the user authentication is unsuccessful.

[0167] S210: After the third user identification fails, the first identification threshold is lowered to the second identification threshold.

[0168] S211: Compare the third consistency parameter with the second identification threshold to perform the fourth user identification.

[0169] S212: After the fourth user identification fails, the feature points of any one of the first response speckle, the second response speckle, and the third response speckle, as well as the feature points of the corresponding standard speckle, are determined based on the feature point matching algorithm, and the first similarity between the two sets of feature points is calculated.

[0170] S213: Compare the first similarity with the third identification threshold to perform the fifth user identification.

[0171] S214: Perform biometric identification on the user after the fifth failed user identification attempt.

[0172] S215: After biometric authentication is successful, obtain activation information indicating that the user has removed and reinserted the PUF, and use the third excitation code to illuminate the user's PUF according to the activation information to obtain the fourth response speckle.

[0173] S216: Based on the feature point matching algorithm, determine the feature points of the fourth response speckle and the feature points of the third standard speckle corresponding to the third excitation code, and calculate the second similarity between the two sets of feature points.

[0174] S217: Compare the second similarity with the third identification threshold to perform a sixth user identification.

[0175] The above S210 to S217 are basically the same as S107 to S114 in the above embodiments. For details, please refer to the above embodiments, and will not be repeated here.

[0176] The user identification method based on optical PUF authentication provided in this embodiment of the invention further verifies the user PUF through more verification methods to improve the repeatability of user identification.

[0177] The specific details of the user identification method based on optical PUF authentication provided by this invention will be described in detail in the following embodiments, and will not be repeated here.

[0178] Example 3

[0179] Please refer to Figure 3 , Figure 3 A flowchart of another specific user identification method based on optical PUF authentication provided in an embodiment of the present invention.

[0180] The method provided in this embodiment is typically executed after the sixth user identification process described above. If the sixth user identification fails verification, then in this embodiment, the fourth response speckle needs to be corrected.

[0181] See Figure 3 In this embodiment, the user identification method based on optical PUF authentication includes:

[0182] S301: Determine the feature point set that matches the feature point set of the fourth response speckle and the feature point set of the third standard speckle.

[0183] In this step, the first step is to determine the feature point sets that match the fourth response speckle and the third standard speckle based on the feature point matching algorithm. When performing feature point detection, the feature point sets of the fourth response speckle and the third standard speckle are typically obtained first. During feature point matching, similar feature point pairs are found in the two images through comparison, forming a matching feature point set. That is, similar feature points are found from the two feature point sets to form feature point pairs. The two feature points constituting a feature point pair are the matching feature point sets in the fourth response speckle and the third standard speckle. The specific details of the feature point matching algorithm have been described in detail in the above embodiments and will not be repeated here.

[0184] S302: Obtain the first set of coordinate values ​​of the feature point set matched in the fourth response speckle and the second set of coordinate values ​​of the feature point set matched in the third standard speckle.

[0185] This step determines the coordinates of each feature point in its respective speckle image. Of course, for the fourth response speckle and the third standard speckle, a coordinate system needs to be established according to the same standard, such as establishing an xy coordinate system with the first pixel in the lower left corner as the origin, etc., but no specific limitation is made here.

[0186] In this step, it is necessary to record the coordinates of the matched feature points in their respective speckle images. The coordinates of the aforementioned feature points in the fourth response speckle are recorded as the first coordinate values, forming a first coordinate value group. Each first coordinate value in this group represents the position of each feature point in the feature point set within the fourth response speckle. The coordinates of the aforementioned feature points in the third standard speckle are recorded as the second coordinate values, forming a second coordinate value group. Each second coordinate value in this group represents the position of each feature point in the feature point set within the third standard speckle. For example, suppose a feature point is detected and matched in the fourth response speckle with coordinates (100, 200), while the corresponding matching feature point in the third standard speckle has coordinates (95, 195).

[0187] S303: Determine the average displacement based on the first set of coordinate values ​​and the second set of coordinate values.

[0188] The aforementioned mean displacement is a value determined by taking the average of the differences between the coordinate values ​​of the matched feature point pairs in their respective images. It represents the overall offset of the fourth response speckle relative to the third standard speckle. In this step, the displacement value corresponding to each feature point is calculated based on the first coordinate value in the first coordinate value group and the second coordinate value corresponding to the matched feature points in the second coordinate value group. Finally, the mean displacement is calculated based on multiple displacement values. Specifically, for each matched feature point pair, the difference between its coordinate value in the fourth response speckle and its coordinate value in the third standard speckle can be calculated, i.e., the displacement vector. Taking the xy coordinate system as an example, the displacement vector can be represented as (Δx, Δy), where Δx is the displacement difference in the horizontal direction and Δy is the displacement difference in the vertical direction.

[0189] Then, the displacement vectors of all matching feature points are averaged to obtain the mean displacement. The mean displacement reflects the overall offset of the fourth response speckle relative to the third standard speckle, and in the xy coordinate system, it typically includes the average offset in both the horizontal and vertical directions.

[0190] For example, suppose there are two sets of matching feature point pairs. The displacement vector of the first set of feature point pairs is (5, 3), and the displacement vector of the second set of feature point pairs is (3, 5). Then the average displacement is ((5+3) / 2, (3+5) / 2), which is (4, 4).

[0191] S304: Based on the displacement mean, translate all pixels in the fourth response speckle to the target coordinate point to obtain the fifth response speckle.

[0192] In this embodiment, the fifth response speckle has an overlapping region with the fourth response speckle. The essence of this step is to correct the fourth response speckle. Therefore, in this step, each pixel in the fourth response speckle is translated from its current coordinate point to the target coordinate point based on the aforementioned displacement mean; or, in other words, the pixel value at the target coordinate point is replaced with the pixel value corresponding to the current coordinate point, thereby obtaining the fifth response speckle. Obviously, in this embodiment, the fifth and fourth response speckles typically share a coordinate system. The image of the fifth response speckle is similar to the image of the fourth response speckle, but the entire image has been translated. After translation in this embodiment, the fifth and fourth response speckles have overlapping and non-overlapping regions in their coordinate systems.

[0193] For example, suppose there is a pixel in the fourth response speckle with coordinates (150, 250) and a pixel value of 128, and a mean displacement of (4, 4). Then the target coordinates of this pixel can be (150+4, 250+4), i.e., (154, 254). Correspondingly, the pixel in the fifth response speckle located at (154, 254) has a pixel value of 128. This step, by performing translation correction on the fourth response speckle, makes the image features of the fifth response speckle and the third standard speckle more consistent in the overlapping area, which is beneficial to improving the robustness of subsequent authentication processes.

[0194] S305: Authentication is performed based on the image of the fifth response speckle located in the overlapping region and the image of the third standard speckle located in the corresponding region, in order to perform the seventh user identification.

[0195] The image of the third standard speckle located in the corresponding region has the same coordinates as the image of the fifth response speckle located in the overlapping region. That is, in this step, the image of the fifth response speckle located in the overlapping region, and the portion of the third standard speckle with coordinates matching the overlapping region, will be used for authentication to perform a seventh user identification. The specific authentication process will be described in detail in the following embodiments of the invention, and will not be repeated here.

[0196] The user identification method based on optical PUF authentication provided in this invention determines the corresponding positions between the response speckle and the standard speckle by feature point matching, and determines the degree of offset of the response speckle by calculating the mean displacement. Then, the response speckle is corrected, and user identification is performed based on the corrected response speckle and a third standard speckle, which can significantly improve the robustness of user identification.

[0197] The specific details of the user identification method based on optical PUF authentication provided by this invention will be described in detail in the following embodiments, and will not be repeated here.

[0198] Example 4

[0199] Please refer to Figure 4 as well as Figure 5 , Figure 4 A flowchart illustrating yet another specific user identification method based on optical PUF authentication provided in this embodiment of the invention; Figure 5 This is a schematic diagram illustrating the generation principle of the fifth response speckle pattern.

[0200] See Figure 4 In this embodiment of the invention, the user identification method based on optical PUF authentication includes:

[0201] S401: Detect the feature point set of the fourth response speckle and the feature point set of the third standard speckle based on the feature point matching algorithm.

[0202] In this step, all feature points of the fourth response speckle will be detected to form a feature point set, and all feature points of the third standard speckle will be detected to form a feature point set.

[0203] S402: Extract the feature points detected in the fourth response speckle and extract the feature point set detected in the third standard speckle.

[0204] In this step, the descriptors corresponding to each feature point in the feature point set detected in the fourth response speckle will be extracted, as well as the descriptors corresponding to each feature point in the feature point set detected in the third standard speckle will be extracted.

[0205] S403: Match the feature point set extracted from the fourth response speckle with the feature point set extracted from the third standard speckle, and filter out the matching feature point sets.

[0206] The specific details of the feature point matching algorithm described above can be customized according to the actual situation, and are not specifically limited here. This step will match the feature points in the two extracted feature point sets. For example, by comparing the descriptors of feature points extracted from different speckle images, the matching feature points in the two sets of feature points will be selected to form a set of matching feature points.

[0207] Following this step, it is necessary to record the coordinates of the matching feature points in their respective speckle images. The specific details of this process have been described in detail in the above embodiments and will not be repeated here. In this embodiment, it is assumed that the feature point set in the fourth response speckle is R, and the feature point set R includes each feature point R1, R2...R... k Let the first coordinates of each feature point in the feature point set R be (M1, N1), (M2, N2), ..., (M...). k N k This forms the first set of coordinate values; assuming the feature point set in the third standard speckle is S, the feature point set S includes each feature point S1, S2...S... k Let the second coordinates of each feature point in the feature point set S be (P1, Q1), (P2, Q2), ..., (P...). k Q k This forms the second set of coordinate values.

[0208] S404: Determine the mean horizontal and vertical displacements based on the first and second coordinate value sets.

[0209] This step calculates the mean x-coordinate displacement m1 and the mean y-coordinate displacement m2 of each feature point in feature point set R relative to each feature point in feature point set S, where:

[0210] m1=[(P1-M1)+(P2-M2)+(P3-M3)+......+(P k -M k )] / k;

[0211] m2=[(Q1-N1)+(Q2-N2)+(Q3-N3)+......+(Q k -N k )] / k.

[0212] Accordingly, in subsequent steps, all pixels in the fourth response speckle are translated to the target coordinate point based on the average horizontal and vertical coordinate displacements to form the fifth response speckle. For example, for pixel (i, j) in the fourth response speckle, its corresponding target coordinate point is (i+m1, j+m2), that is, the pixel value of pixel (i, j) in the fourth response speckle is the same as the pixel value of pixel (i+m1, j+m2) in the fifth response speckle.

[0213] S405: Generate a blank image of the same size as the fourth response speckle pattern.

[0214] In this embodiment, the pixel value of each pixel in the blank image is 0. That is, in this step, an image in which all pixels are 0 is first generated, and the size of this blank image is usually the same as that of the fourth response speckle.

[0215] S406: Determine the target coordinates of each pixel in the fourth response speckle based on the average displacement and the current coordinates of each pixel in the fourth response speckle.

[0216] See Figure 5 In this step, the average displacement calculated in the previous steps is added to the current coordinates of each pixel in the fourth response speckle to obtain the target coordinates of each pixel. For example, for pixel (i, j) in the fourth response speckle, its corresponding target coordinates are (i+m1, j+m2).

[0217] S407: Fill the pixel values ​​of each pixel in the fourth response speckle with the corresponding target coordinate values ​​of the pixels in the blank image to obtain the fifth response speckle.

[0218] In this step, the pixel value V of pixel (i, j) in the fourth response speckle will be... ij Fill the blank image with pixels (i+m1, j+m2) so that the pixel value of pixels (i+m1, j+m2) in the blank image is also V. ij Thus, the fifth response speckle pattern is obtained.

[0219] S408: Based on the displacement mean, the fifth response speckle and the third standard speckle are trimmed to obtain the final response speckle that does not exceed the overlapping area, and the final standard speckle whose position corresponds to the final response speckle.

[0220] In this step, the fifth response speckle and the third standard speckle will be cropped. The specific cropping process should refer to the aforementioned displacement average. When translating the fourth response speckle to obtain the fifth response speckle, there is usually an overlapping area between the fourth and fifth response speckles in the same coordinate system. In this step, the fifth response speckle needs to be cropped so that the coordinates of each pixel in the final response speckle do not exceed the range of the overlapping area. For example, when cropping the fifth response speckle, only pixels with abscissas in the range of (1+m1, i-m1) and ordinates in the range of (1+m2, j-m2) are retained. This pixel region does not exceed the overlapping area, ensuring that the cropped final response speckle does not exceed the overlapping area. Correspondingly, in this step, the third standard speckle also needs to be cropped to obtain the final standard speckle. The pixel region corresponding to the final standard speckle needs to correspond to the pixel region of the final response speckle. Therefore, during cropping, only the pixel region with the horizontal coordinate range of (1+m1, i-m1) and the vertical coordinate range of (1+m2, j-m2) can be retained to obtain the final standard speckle. Of course, in this embodiment, the cropped region is not specifically limited. For example, it can be further reduced based on the above coordinate range, which is not specifically limited here. Correspondingly, in subsequent steps, authentication needs to be performed based on the final response speckle and the final standard speckle.

[0221] S409: Based on the image filtering algorithm, determine the sixth binarized texture feature image corresponding to the final response speckle, and the seventh binarized texture feature image corresponding to the final standard speckle.

[0222] In this step, image filtering algorithms such as Gabor filtering can be used to process the two types of speckle images mentioned above. For details on image filtering algorithms such as Gabor filtering, please refer to existing technologies; they will not be elaborated upon here. In this step, the final response speckle and the final standard speckle can be processed separately using the Gabor filtering algorithm to obtain the sixth binarized texture feature image corresponding to the final response speckle and the seventh binarized texture feature image corresponding to the final standard speckle.

[0223] S410: Determine the fourth consistency parameter between the sixth binarized texture feature image and the seventh binarized texture feature image.

[0224] In this step, a consistency parameter can be determined between the sixth and seventh binarized texture feature images, serving as the fourth consistency parameter. This fourth consistency parameter characterizes the degree of consistency or similarity between the sixth and seventh binarized texture feature images. This primary parameter can be either the Pearson correlation coefficient or the Hamming distance, etc. For the specific calculation process of Hamming distance or Pearson correlation coefficient, please refer to existing techniques, which will not be elaborated here. This fourth consistency parameter characterizes the degree of difference between the sixth and seventh binarized texture feature images.

[0225] S411: Determine whether user authentication is successful based on the fourth consistency parameter.

[0226] Taking Hamming distance as an example, the smaller the Hamming distance, the higher the similarity between the two images. In this step, when the fourth Hamming distance is less than the target threshold, it means that the fourth response speckle has a high similarity to the third standard speckle, and the user authentication can be determined to be successful. Conversely, when the fourth Hamming distance is greater than or equal to the target threshold, it means that the fourth response speckle has a large difference from the third standard speckle, and the user authentication can be determined to be unsuccessful. Taking Pearson correlation coefficient as an example, the larger the Pearson correlation coefficient, the higher the similarity between the two images. In this step, when the Pearson correlation coefficient is greater than the target threshold, it means that the first response speckle has a high similarity to the standard speckle, and the user authentication can be determined to be successful. Conversely, when the Pearson correlation coefficient is less than or equal to the target threshold, it means that the first response speckle has a large difference from the standard speckle, and the user authentication can be determined to be unsuccessful.

[0227] The target thresholds mentioned above need to be determined based on the type of consistency parameter. For example, for the Hamming distance, the target threshold could be 0.3; for the Pearson correlation coefficient, the target threshold could be 0.7. The specific values ​​of the target thresholds can be set according to the actual situation, and are not specifically limited here.

[0228] The user identification method based on optical PUF authentication provided in this invention can effectively solve the authentication speckle offset problem caused by factors such as authentication system instability and repeated plugging and unplugging of PUF by users during the optical PUF authentication process. This improves the stability and robustness of the optical PUF authentication system, ensures that legitimate users can pass authentication smoothly, and prevents unauthorized access by unauthorized users, thereby enhancing the security of the system.

[0229] Example 5

[0230] The following describes a user identification device based on optical PUF authentication provided by an embodiment of the present invention. The user identification device based on optical PUF authentication described below can be referred to in correspondence with the user identification method based on optical PUF authentication described above.

[0231] Please refer to Figure 6 , Figure 6 This is a structural block diagram of a user identification device based on optical PUF authentication provided in an embodiment of the present invention.

[0232] See Figure 6 In this embodiment of the invention, the user identification device based on optical PUF authentication may include:

[0233] The first response speckle module 100 is used to illuminate the user PUF with a first excitation code to obtain the first response speckle;

[0234] The first user identification module 200 is used to determine a first consistency parameter between the first response speckle and the first standard speckle corresponding to the first excitation code, so as to perform the first user identification based on the first consistency parameter and the first identification threshold.

[0235] The second response speckle module 300 is used to re-illuminate the user PUF using the same first excitation code after the first user identification fails, and to continuously acquire multiple second response speckles.

[0236] The second user identification module 400 is used to determine a second consistency parameter between each of the second response speckles and the first standard speckles, so as to perform a second user identification based on a plurality of second consistency parameters and the first identification threshold;

[0237] The third response speckle module 500 is used to illuminate the user's PUF sequentially with multiple different second excitation codes after the second user identification fails, and to obtain the third response speckle after illuminating the user's PUF with each second excitation code; each second excitation code corresponds to a second standard speckle.

[0238] The third user identification module 600 is used to determine a third consistency parameter between each of the third response speckles and the second standard speckles corresponding to the second excitation code, so as to perform a third user identification based on the multiple third consistency parameters and the first identification threshold.

[0239] The threshold reduction module 700 is used to reduce the first recognition threshold to the second recognition threshold after the third user recognition fails.

[0240] The fourth user identification module 800 is used to compare the third consistency parameter with the second identification threshold to perform a fourth user identification.

[0241] The first similarity module 900 is used to determine the feature point set of any one of the first response speckle, the second response speckle, and the third response speckle, as well as the feature point set of the corresponding standard speckle, based on the feature point matching algorithm after the fourth user identification fails, and to calculate the first similarity between the two sets of feature point sets.

[0242] The fifth user identification module 1000 is used to compare the first similarity with the third identification threshold to perform the fifth user identification.

[0243] The biometric module 1100 is used to perform biometric identification on the user after the fifth failed user identification attempt.

[0244] The fourth response speckle module 1200 is used to acquire activation information representing the user removing and reinserting the PUF after biometric recognition is successful, and to illuminate the user's PUF with a third excitation code according to the activation information to acquire the fourth response speckle.

[0245] The second similarity module 1300 is used to determine the feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, and to calculate the second similarity between the two sets of feature point sets.

[0246] The sixth user identification module 1400 is used to compare the second similarity with the third identification threshold to perform a sixth user identification.

[0247] Preferably, in this embodiment of the invention, it further includes:

[0248] The feature point matching module is used to determine the feature point set that matches each other between the feature point set of the fourth response speckle and the feature point set of the third standard speckle;

[0249] The coordinate value module is used to obtain the first coordinate value set of the feature point set matched in the fourth response speckle and the second coordinate value set of the feature point set matched in the third standard speckle.

[0250] The displacement mean module is used to determine the displacement mean based on the first coordinate value group and the second coordinate value group;

[0251] The fifth response speckle module is used to translate all pixels in the fourth response speckle to the target coordinate point according to the displacement mean, thereby obtaining the fifth response speckle; the fifth response speckle has an overlapping area with the fourth response speckle;

[0252] The seventh user identification module is used to perform authentication based on the image of the fifth response speckle located in the overlapping region and the image of the third standard speckle located in the corresponding region, so as to perform the seventh user identification.

[0253] Preferably, in this embodiment of the invention, the first similarity module includes:

[0254] The first unit of measurement is used to determine the number of feature points that match between the two sets of feature points.

[0255] The first similarity unit is used to determine the percentage of the number of mutually matching feature points to the total number of feature points, which is used as the first similarity.

[0256] And / or, the second similarity module includes:

[0257] The second unit of measurement is used to determine the number of feature points that match between the two sets of feature points.

[0258] The second similarity unit is used to determine the percentage of mutually matching feature points out of the total number of feature points, which is used as the second similarity.

[0259] Preferably, in this embodiment of the invention, the second user identification module is specifically used for:

[0260] When any one of the second consistency parameters exceeds the first identification threshold, the second user identification is determined to be successful;

[0261] And / or, the third-party user identification module is specifically used for:

[0262] When any of the third consistency parameters exceeds the first identification threshold, the third user identification is determined to be successful;

[0263] And / or, the fourth user identification module is specifically used for:

[0264] When any of the third consistency parameters exceeds the second identification threshold, the fourth user identification is determined to be successful.

[0265] Preferably, in this embodiment of the invention, the first user identification module is specifically used for:

[0266] The Pearson correlation coefficient between the first response speckle and the first standard speckle corresponding to the first excitation code is determined as the first consistency parameter.

[0267] And / or, the second user identification module is used for:

[0268] The Pearson correlation coefficient between each of the second response speckles and the first standard speckle is determined as the second consistency parameter;

[0269] And / or, the third-party user identification module is specifically used for:

[0270] The Pearson correlation coefficient between each of the third response speckles and the second standard speckles based on the second excitation code is determined as the third consistency parameter.

[0271] Preferably, in this embodiment of the invention, the first user identification module includes:

[0272] The first filtering unit is used to determine, based on an image filtering algorithm, the first binarized texture feature image of the first response speckle and the second binarized texture feature image of the first standard speckle;

[0273] The first consistency parameter unit is used to determine the first consistency parameter between the first binarized texture feature image and the second binarized texture feature image;

[0274] And / or, the second user identification module includes:

[0275] The second filtering unit is used to determine the third binarized texture feature image of each of the second response speckles and the second binarized texture feature image of the first standard speckle based on the image filtering algorithm.

[0276] The second consistency parameter unit is used to determine the second consistency parameter between the second binarized texture feature image and each of the third binarized texture feature images;

[0277] And / or, the third-party user identification module includes:

[0278] The third filtering unit is used to determine the fourth binarized texture feature image of each of the third response speckles and the fifth binarized texture feature image of each of the second standard speckles based on the image filtering algorithm.

[0279] The third consistency parameter unit is used to determine the third consistency parameter between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image.

[0280] Preferably, in this embodiment of the invention, the first consistency parameter unit is specifically used for:

[0281] The Hamming distance between the first binarized texture feature image and the second binarized texture feature image is determined as the first consistency parameter;

[0282] Alternatively, the Pearson correlation coefficient between the first binarized texture feature image and the second binarized texture feature image can be determined as the first consistency parameter;

[0283] The second consistency parameter unit is specifically used for:

[0284] The Hamming distance between the second binarized texture feature image and each of the third binarized texture feature images is determined as the second consistency parameter;

[0285] Alternatively, the Pearson correlation coefficient between the second binarized texture feature image and each of the third binarized texture feature images can be determined as the second consistency parameter;

[0286] The third consistency parameter unit is specifically used for:

[0287] The Hamming distance between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image is determined as the third consistency parameter;

[0288] Alternatively, the Pearson correlation coefficient between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image can be determined as the third consistency parameter.

[0289] The user identification device based on optical PUF authentication in this embodiment is used to implement the aforementioned user identification method based on optical PUF authentication. Therefore, the specific implementation of the user identification device based on optical PUF authentication can be found in the embodiment section of the user identification method based on optical PUF authentication mentioned above. For example, the first response speckle module 100, the first user identification module 200, the second response speckle module 300, the second user identification module 400, the third response speckle module 500, the third user identification module 600, the threshold reduction module 700, the fourth user identification module 800, the first similarity module 900, the fifth user identification module 1000, the biometric module 1100, the fourth response speckle module 1200, the second similarity module 1300, and the sixth user identification module 1400 are respectively used to implement steps S101 to S114 in the aforementioned user identification method based on optical PUF authentication. Therefore, its specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0290] Example 6

[0291] The following describes a user identification device based on optical PUF authentication provided by an embodiment of the present invention. The user identification device based on optical PUF authentication described below can be referred to in correspondence with the user identification method based on optical PUF authentication and the user identification device based on optical PUF authentication described above.

[0292] Please refer to Figure 7 , Figure 7This is a structural block diagram of a user identification device based on optical PUF authentication provided in an embodiment of the present invention.

[0293] Reference Figure 7 The user identification device based on optical PUF authentication may include a processor 11 and a memory 12.

[0294] The memory 12 is used to store computer programs; the processor 11 is used to execute the computer programs to implement the user identification method based on optical PUF authentication as described in the above embodiments of the invention.

[0295] In this embodiment of the user identification device based on optical PUF authentication, the processor 11 is used to install the user identification device based on optical PUF authentication described in the above embodiments. Simultaneously, the processor 11, combined with the memory 12, can implement the user identification method based on optical PUF authentication described in any of the above embodiments. Therefore, the specific implementation of the user identification device based on optical PUF authentication can be found in the embodiments section of the user identification method based on optical PUF authentication described above. The specific implementation can be referred to the descriptions of the corresponding embodiments, and will not be repeated here.

[0296] Example 7

[0297] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a user identification method based on optical PUF authentication as described in any of the above embodiments. Further details can be found in the prior art and will not be elaborated upon here.

[0298] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0299] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0300] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0301] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0302] The foregoing has provided a detailed description of a user identification method and related apparatus based on optical PUF authentication provided by this invention. Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and core ideas of this invention. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this invention.

Claims

1. A user identification method based on optical PUF authentication, characterized in that, include: Illuminate the user's PUF with the first incentive code to obtain the first response speckle; A first consistency parameter is determined between the first response speckle and the first standard speckle corresponding to the first excitation code, so as to perform the first user identification based on the first consistency parameter and the first identification threshold; After the first user identification fails, the same first stimulus code is used to re-illuminate the user PUF, and multiple second response speckles are continuously acquired; A second consistency parameter is determined between each of the second response speckles and the first standard speckle, so as to perform a second user identification based on the multiple second consistency parameters and the first identification threshold; After the second user identification fails, multiple different second incentive codes are used to illuminate the user's PUF in sequence, and a third response speckle is obtained after each second incentive code is used to illuminate the user's PUF; each second incentive code corresponds to a second standard speckle. A third consistency parameter is determined between each of the third response speckles and the second standard speckles corresponding to the second excitation code, so as to perform a third user identification based on the plurality of third consistency parameters and the first identification threshold; After the third user identification fails, the first identification threshold is lowered to the second identification threshold; The third consistency parameter is compared with the second identification threshold to perform a fourth user identification. After the fourth user identification fails, the feature point set of any one of the first response speckle, the second response speckle, and the third response speckle, as well as the feature point set of the corresponding standard speckle, are determined based on the feature point matching algorithm, and the first similarity between the two sets of feature point sets is calculated. The first similarity is compared with the third identification threshold to perform a fifth user identification. After the fifth failed user identification attempt, biometric identification will be performed on the user. Once biometric authentication is successful, activation information representing the user removing and reinserting the PUF is acquired, and the user's PUF is illuminated with a third excitation code based on the activation information to acquire a fourth response speckle pattern. The feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third excitation code are determined based on the feature point matching algorithm, and the second similarity between the two feature point sets is calculated. The second similarity is compared with the third identification threshold to perform a sixth user identification.

2. The method according to claim 1, characterized in that, After determining the feature point set of the fourth response speckle and the feature point set of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, the method further includes: Determine the feature point set that matches the feature point set of the fourth response speckle and the feature point set of the third standard speckle; Obtain the first set of coordinate values ​​of the feature point set matched in the fourth response speckle, and the second set of coordinate values ​​of the feature point set matched in the third standard speckle; The average displacement is determined based on the first set of coordinate values ​​and the second set of coordinate values. Based on the displacement mean, all pixels in the fourth response speckle are translated to the target coordinate point to obtain the fifth response speckle; the fifth response speckle has an overlapping area with the fourth response speckle; The authentication is performed based on the image of the fifth response speckle located in the overlapping region and the image of the third standard speckle located in the corresponding region, in order to perform the seventh user identification.

3. The method according to claim 1, characterized in that, Calculating the first similarity between two sets of feature points includes: Determine the number of feature points that match between the two sets of feature points; The percentage of mutually matching feature points out of the total number of feature points is used as the first similarity. And / or, calculating the second similarity between two sets of feature points includes: Determine the number of feature points that match between the two sets of feature points; The percentage of mutually matching feature points out of the total number of feature points is determined as the second similarity.

4. The method according to claim 1, characterized in that, The second user identification based on multiple second consistency parameters and the first identification threshold includes: When any one of the second consistency parameters exceeds the first identification threshold, the second user identification is determined to be successful; And / or, performing a third user identification based on a plurality of the third consistency parameters and the first identification threshold includes: When any of the third consistency parameters exceeds the first identification threshold, the third user identification is determined to be successful; And / or, performing a fourth user identification includes: When any of the third consistency parameters exceeds the second identification threshold, the fourth user identification is determined to be successful.

5. The method according to claim 1, characterized in that, The first consistency parameter for determining the first response speckle and the first standard speckle corresponding to the first excitation code includes: The Pearson correlation coefficient between the first response speckle and the first standard speckle corresponding to the first excitation code is determined as the first consistency parameter. And / or, determining the second consistency parameter between each of the second response speckles and the first standard speckle includes: The Pearson correlation coefficient between each of the second response speckles and the first standard speckle is determined as the second consistency parameter; And / or, determining the third consistency parameter between each of the third response speckles and the second standard speckles based on the second excitation code includes: The Pearson correlation coefficient between each of the third response speckles and the second standard speckles based on the second excitation code is determined as the third consistency parameter.

6. The method according to claim 1, characterized in that, The first consistency parameter for determining the first response speckle and the first standard speckle corresponding to the first excitation code includes: Based on the image filtering algorithm, the first binarized texture feature image of the first response speckle and the second binarized texture feature image of the first standard speckle are determined; Determine a first consistency parameter between the first binarized texture feature image and the second binarized texture feature image; And / or, determining the second consistency parameter between each of the second response speckles and the first standard speckle includes: Based on the image filtering algorithm, the third binarized texture feature image of each second response speckle is determined to be the same as the second binarized texture feature image of the first standard speckle. Determine a second consistency parameter between the second binarized texture feature image and each of the third binarized texture feature images; And / or, determining the third consistency parameter between each of the third response speckles and the second standard speckles based on the second excitation code includes: Based on the image filtering algorithm, the fourth binarized texture feature image of each of the third response speckles and the fifth binarized texture feature image of each of the second standard speckles are determined. A third consistency parameter is determined between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image.

7. The method according to claim 6, characterized in that, The first consistency parameter for determining the relationship between the first binarized texture feature image and the second binarized texture feature image includes: The Hamming distance between the first binarized texture feature image and the second binarized texture feature image is determined as the first consistency parameter; Alternatively, the Pearson correlation coefficient between the first binarized texture feature image and the second binarized texture feature image can be determined as the first consistency parameter; The second consistency parameter for determining the second binarized texture feature image and each of the third binarized texture feature images includes: The Hamming distance between the second binarized texture feature image and each of the third binarized texture feature images is determined as the second consistency parameter; Alternatively, the Pearson correlation coefficient between the second binarized texture feature image and each of the third binarized texture feature images can be determined as the second consistency parameter; The third consistency parameter for determining each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image includes: The Hamming distance between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image is determined as the third consistency parameter; Alternatively, the Pearson correlation coefficient between each of the fourth binarized texture feature images and the corresponding fifth binarized texture feature image can be determined as the third consistency parameter.

8. A user identification device based on optical PUF authentication, characterized in that, include: The first response speckle module is used to illuminate the user PUF with the first excitation code to obtain the first response speckle; The first user identification module is used to determine a first consistency parameter between the first response speckle and the first standard speckle corresponding to the first excitation code, so as to perform the first user identification based on the first consistency parameter and the first identification threshold. The second response speckle module is used to re-illuminate the user PUF using the same first excitation code after the first user identification fails, and to continuously acquire multiple second response speckles. The second user identification module is used to determine a second consistency parameter between each of the second response speckles and the first standard speckles, so as to perform a second user identification based on a plurality of second consistency parameters and the first identification threshold; The third response speckle module is used to illuminate the user's PUF sequentially with multiple different second excitation codes after the second user identification fails, and to obtain the third response speckle after illuminating the user's PUF with each second excitation code; each second excitation code corresponds to a second standard speckle. The third user identification module is used to determine the third consistency parameter between each of the third response speckles and the second standard speckles corresponding to the second excitation code, so as to perform the third user identification based on the multiple third consistency parameters and the first identification threshold. The threshold reduction module is used to reduce the first recognition threshold to the second recognition threshold after the third user recognition fails. The fourth user identification module is used to compare the third consistency parameter with the second identification threshold to perform a fourth user identification. The first similarity module is used to determine the feature points of any one of the first response speckle, the second response speckle, and the third response speckle, as well as the feature points of the corresponding standard speckle, based on the feature point matching algorithm after the fourth user identification fails, and to calculate the first similarity between the two sets of feature points. The fifth user identification module is used to compare the first similarity with the third identification threshold to perform a fifth user identification. The biometric module is used to perform biometric identification on the user after the fifth failed user identification attempt. The fourth response speckle module is used to acquire activation information representing the user removing and reinserting the PUF after biometric recognition is successful, and to illuminate the user's PUF with a third excitation code according to the activation information to acquire the fourth response speckle. The second similarity module is used to determine the feature points of the fourth response speckle and the feature points of the third standard speckle corresponding to the third excitation code based on the feature point matching algorithm, and to calculate the second similarity between the two sets of feature points. The sixth user identification module is used to compare the second similarity with the third identification threshold to perform a sixth user identification.

9. A user identification device based on optical PUF authentication, characterized in that, The device includes: Memory: Used to store computer programs; Processor: configured to implement the steps of the user identification method based on optical PUF authentication as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the user identification method based on optical PUF authentication as described in any one of claims 1 to 7.

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